Behavioral Economics of AI: LLM Biases and Corrections

Pietro Bini, Lin Cong, Xing Huang and Lawrence Jin

No 34745, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: Do generative AI models, particularly large language models (LLMs), exhibit systematic behavioral biases in economic and financial decisions? If so, how can these biases be mitigated? Drawing on the cognitive psychology and experimental economics literatures, we conduct the most comprehensive set of experiments to date—originally designed to document human biases—on prominent LLM families across model versions and scales. We document systematic patterns in LLM behavior. In preference-based tasks, responses become more human-like as models become more advanced or larger, while in belief-based tasks, advanced large-scale models frequently generate rational responses. Prompting LLMs to make rational decisions reduces biases.

JEL-codes: D03 G02 G11 G4 G40 G41 (search for similar items in EconPapers)
Date: 2026-01
Note: AP
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